How brands can find and keep customers without third-party data
With third-party data diminished by GDPR, Apple, Google and issues of quality, some marketers want to live primarily on first-party data.
If you come back in the next world as a kind of data, try to avoid coming back as third-party data.
That’s because third-party data — collected in almost every way except a direct contact between a brand and its customers — is rapidly falling out of favor. It’s battling the new General Data Protection Regulation (GDPR), restrictions from Apple’s and Google’s browsers, and a general sense among brands that third-party data isn’t the highest quality.
As we head into what is apparently a new era for customer data, in fact, a common refrain among some data providers, particularly Customer Data Platforms (CDPs), is that first-party data — directly collected by a brand about its customers or visitors — is the kind that brands should focus on.
But can brands live on first-party data alone?
Yes, says James McDermott, CEO and co-founder of CDP Lytics.
First-party data represents a brand’s “family” — its fans, occasional customers and visitors — and thus, is the audience that is most interested in what the brand offers. It makes sense, then, that brands will get their biggest return by marketing to this group.
But how does a brand acquire new customers if it doesn’t target prospects by their third-party attributes?
Take care of family
“Third-party data is a challenge,” McDermott said, pointing to its inaccuracy and the increasingly difficult prospect of using personal data for which users haven’t given consent.
Although third-party data doesn’t commonly include “personally identifiable information” (PII) like a name, street address or Social Security number, it can include IP address, web browsing behavior, products purchased and other info that GDPR considers “personal data” because, taken together, they can point to an individual.
Plus, there’s the fact that not every brand has a sizable “family” of fans. There are dozens of brands in my life, for instance, but I’m only involved in a “relationship” with a few of them, like Apple, Trader Joe’s and Netflix. Offhand, I don’t even know the names of the brands that make the machines which wash and dry our clothes, for instance, and, until this moment, I’ve never thought about the brands of breads in our breadbox.
To get away from a reliance on third-party data, McDermott said, brands first need to take care of their family.
It’s not enough for a brand to just communicate with its customers, he said. The brand needs to address the clues that the family members have provided, like the products each member likes, when they like them, how they like them and the hints about new products or services they might similarly like.
To get new customers beyond your family without employing third-party data that targets possible customers based on demographics, income or other anonymized attributes, McDermott told me, brands need to get creative.
First, he pointed out, brands can focus their advertising toward context, such as ads about cars on a web page about new cars, instead of ads about cars delivered to visitors whose demographics and visits to a car site indicated their interest. Special discounts or other offers can entice new customers into the brand.
The other major way to acquire new customers outside your family without going third-party, he noted, is by employing lookalikes.
The first-party data of your best customers, for instance, becomes a “seed” audience. Their identifiers, such as email addresses, are uploaded to Facebook and Google, which then find similar prospects having similar attributes. (For GDPR compliance, of course, consent for such use would need to be expressly given.)
McDermott noted that Facebook and Google users should have already given consent for their personal data to be matched in this way, and the brand doesn’t see the attributes or targeting parameters. The brand is asking these enormous walled gardens to find similar people without specifying the matching characteristics, on the assumption that similar people will be interested in similar products.
This is different, he contended, from giving my first-party list to a data service, where the brand needs to assess the attributes being matched. Those matches in a non-Facebook or Google environment, he said, are to personal data whose users have often not provided direct consent.
A lookalike model with the Big Two “is more privacy-friendly” for a brand, McDermott said, than employing anonymized third-party data. Of course, this means that Facebook and Google become more powerful as the other part of a brand’s outreach from its known best customers.
This can gin up new customers, and brands can keep them as part of the family by better loyalty programs, or by subscriptions. McDermott pointed to the growing interest among brands for direct-to-consumer sales as a way of creating a fanbase for even mundane products. His example: the Dollar Shave Club, which ships a physical box of renewable shaving products to its “members,” thus creating an ongoing relationship between customers and consumer packaged goods.
There’s also another up-and-coming way to grow a brand without third-party data: intelligent agents. Amazon Alexa and Google Home, for instance, may search for “lookalike brands” on my behalf when I’m looking for a new coffee maker, as the agents try to find similar characteristics to the one that just broke down.
It’s the reverse of how a brand might reach lookalike audiences, but it confirms that, in the era after third-party data, brand relationships can become a two-way street.
Opinions expressed in this article are those of the guest author and not necessarily MarTech. Staff authors are listed here.